Language Agent Tree Search Unifies Reasoning Acting and Planning in Language Models (2310.04406v3)

Abstract: While LLMs (LMs) have shown potential across a range of decision-making tasks, their reliance on simple acting processes limits their broad deployment as autonomous agents. In this paper, we introduce Language Agent Tree Search (LATS) -- the first general framework that synergizes the capabilities of LMs in reasoning, acting, and planning. By leveraging the in-context learning ability of LMs, we integrate Monte Carlo Tree Search into LATS to enable LMs as agents, along with LM-powered value functions and self-reflections for proficient exploration and enhanced decision-making. A key feature of our approach is the incorporation of an environment for external feedback, which offers a more deliberate and adaptive problem-solving mechanism that surpasses the constraints of existing techniques. Our experimental evaluation across diverse domains, including programming, interactive question-answering (QA), web navigation, and math, validates the effectiveness and generality of LATS in decision-making while maintaining competitive or improved reasoning performance. Notably, LATS achieves state-of-the-art pass@1 accuracy (92.7%) for programming on HumanEval with GPT-4 and demonstrates gradient-free performance (average score of 75.9) comparable to gradient-based fine-tuning for web navigation on WebShop with GPT-3.5. Code can be found at https://github.com/lapisrocks/LanguageAgentTreeSearch

Overview of "Language Agent Tree Search Unifies Reasoning Acting and Planning in LLMs"

The paper "Language Agent Tree Search Unifies Reasoning Acting and Planning in LLMs" introduces LATS (Language Agent Tree Search), a framework positioned at the intersection of reasoning, acting, and planning within LLMs. This framework leverages the capabilities of LLMs to function as intelligent agents that can effectively plan, act, and reason within diverse domains. LATS distinguishes itself by drawing parallels with Monte Carlo Tree Search (MCTS) methods commonly used in model-based reinforcement learning. The central thesis is that, by integrating LLMs with MCTS-style planning, a synergistic model emerges, capable of surpassing traditional LLM paradigms for decision-making, particularly in situations requiring adaptive problem-solving and interactive feedback.

Key Contributions

1. Integration of Planning and Reasoning: LATS combines planning and reasoning abilities by expanding on previous prompting methods, such as Chain-of-Thought (CoT) and ReAct. It employs LM as an agent that generates possible actions and thoughts, while a value function evaluates these, determining the most promising paths.
2. Model Architecture: The framework effectively adapts LLMs for each role in the MCTS structure: agent, value function, and optimizer. This design repurposes established LLM capabilities, without additional training, facilitating model-free exploratory planning.
3. Adaptability through Feedback: LATS innovatively includes external feedback, both from interactive environments and self-reflections. This external reference enables a dynamic learning mechanism, refining the model's reasoning processes iteratively.
4. Applied Results: The paper showcases strong empirical performance across various domains: programming (achieving a Pass@1 rate of 94.4% on HumanEval with GPT-4), question answering on HotPotQA, and web navigation with WebShop. Notably, in web navigation tasks, LATS raises the average score significantly, indicating its broad applicability.

Technical Results and Implications

• Competitive Results: The framework's ability to double GPT-3.5's performance on HotPotQA over ReAct highlights its efficacy. The significant improvements in programming challenges, as evidenced by the HumanEval scores, emphasize the model's robustness in iterative problem-solving environments.
• Search Algorithm: Leveraging MCTS within LATS, the model navigates through action and reasoning trajectories more effectively than traditional LLM approaches. The use of UCT (Upper Confidence bounds for Trees) as a heuristic evaluation metric further enhances selection strategy in decision-making processes.

Speculations on Future Developments

The paper suggests substantial implications for future AI models, pointing towards increasingly autonomous systems capable of sophisticated task completion across unstructured domains. The integration of feedback mechanisms offers potential pathways for further improving model adaptability and alignment with human-like reasoning. Additionally, ongoing improvements in computational resources and LLM architectures might allow more complex environments and tasks to be addressed effectively with frameworks like LATS.

Overall, the introduction of LATS potentially sets a precedent for enriched LLM development directed at unifying aspects of reasoning, planning, and action, enabling more integrated solutions in complex, multi-faceted problem spaces. Future pathways might explore the scalability of such frameworks across broader AI applications or optimizing LATS for efficiency in resource-constrained scenarios.